D2SC ICMAs

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ICMA 09/04/2025

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```r
plot(cars)
```

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```r
plot(cars)

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<img src=:image/png;base64

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-->


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```r
library(tidyverse)

Warning: package ‘tidyverse’ was built under R version 4.3.3Warning: package ‘tibble’ was built under R version 4.3.3Warning: package ‘tidyr’ was built under R version 4.3.3Warning: package ‘readr’ was built under R version 4.3.3Warning: package ‘purrr’ was built under R version 4.3.3Warning: package ‘dplyr’ was built under R version 4.3.3Warning: package ‘stringr’ was built under R version 4.3.3Warning: package ‘forcats’ was built under R version 4.3.3Warning: package ‘lubridate’ was built under R version 4.3.3── Attaching core tidyverse packages ─────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   4.0.0     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     ── Conflicts ───────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors

library(dplyr)
library(palmerpenguins)

Warning: package ‘palmerpenguins’ was built under R version 4.3.3

library(nycflights13)

Warning: package ‘nycflights13’ was built under R version 4.3.3

library(ggplot2)

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<img src=\data:image/png;base64

view(penguins)

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<img src=\data:image/png;base64

glimpse(flights)

Rows: 336,776
Columns: 19
$ year           <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, …
$ month          <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ day            <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ dep_time       <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 558, 558, 558, 558, 559, 559, 559, 600, 600, 6…
$ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 600, 600, 600, 600, 600, 559, 600, 600, 600, 6…
$ dep_delay      <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2, -1, 0, -1, 0, 0, 1, -8, -3, -4, -4, 0, 8, 1…
$ arr_time       <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 849, 853, 924, 923, 941, 702, 854, 851, 837, …
$ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 851, 856, 917, 937, 910, 706, 902, 858, 825, …
$ arr_delay      <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7, -14, 31, -4, -8, -7, 12, -6, -8, 16, -12, -…
$ carrier        <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6", "AA", "B6", "B6", "UA", "UA", "AA", "B6", …
$ flight         <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301, 49, 71, 194, 1124, 707, 1806, 1187, 371, 4…
$ tailnum        <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N39463", "N516JB", "N829AS", "N593JB", "N3ALA…
$ origin         <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LGA", "JFK", "LGA", "JFK", "JFK", "JFK", "EWR"…
$ dest           <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IAD", "MCO", "ORD", "PBI", "TPA", "LAX", "SFO"…
$ air_time       <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149, 158, 345, 361, 257, 44, 337, 152, 134, 147…
$ distance       <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 733, 1028, 1005, 2475, 2565, 1389, 187, 2227, …
$ hour           <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, …
$ minute         <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59, 0, 0, 0, 0, 10, 5, 10, 10, 7, 0, 0, 10, 15…
$ time_hour      <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-01 …

flights |>
  filter(month == 1 & day == 1)

flights_houston <- flights |>
  filter(dest %in% c("IAH", "HOU"))

```{r}

Error: attempt to use zero-length variable name

flights |>
  mutate(speed = distance / air_time * 60,
         .after = distance) |>
  head()

flights |>
  rename(dep_airport = origin,
         arr_airport = dest)
  select(year, month, day, contains("dep"))
  head()
  
flights |>
  select(!year:day)
  head()

flights |>
  group_by(month) |>
  count()

flights |>
  group_by(month) |>
  summarize(avg_delay = mean(dep_delay))

flights |>
  group_by(month) |>
  summarize(avg_arr_delay = mean(arr_delay, na.rm = TRUE),
          sd_arr_delay = sd(arr_delay, na.rm = TRUE),
          number_delayed = sum(arr_delay > 0, na.rm = TRUE),
          total = n(),
          percentage = number_delayed/total * 100
  ) |>
  ggplot(mapping = aes(x = month,
                       y = percentage))

ICMA 9/23/2025

• Variables can’t start with a number or include spaces or symbols
• Style we’ll use is snake_case

`1number` <- 12
print(`1number`)

[1] 12

library(nycflights13)
summary <- flights |>
  group_by(tailnum) |>
    summarize(avg_arr_delay = mean(arr_delay, na.rm=TRUE), num_flights = n())

Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument
Error in assign(cacheKey, frame, .rs.CachedDataEnv) : 
  attempt to use zero-length variable name

summary

#| label: style-example
#| echo: TRUE

library(nycflights13)
summary <- flights |>
  group_by(tailnum) |>
    summarize(avg_arr_delay = mean(arr_delay, na.rm=TRUE), num_flights = n())

Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument
Error in assign(cacheKey, frame, .rs.CachedDataEnv) : 
  attempt to use zero-length variable name

summary

ICMA 9/25/2025

billboard_long <- billboard |>
  pivot_longer(cols = wk1:wk76,
               names_to = "week",
               values_to = "rank")

billboard |>
  filter(artist == "2 Pac") |>
  select(wk10)

ICMA 09/30/2025

• Simpson’s Paradox: trend in overall data differs from trends in individual groups

```{r}

Error: attempt to use zero-length variable name

ICMA 10/07/2025

• Layers
  • Data: the dataframe you’re plotting from
  • Aesthetic: specific things in the dataframe to be mapped to aspects of the data visualization: x-values, y-values, size-variation, color-categories, etc.
  • geom: a geometry representing data (line, points,etc.)
  • statistics: transformations to be plotted (means, medians, etc.)
  • facets: splitting the dataset based on some variable or category and providing a separate plot for each split
  • coordinates: numeric system to determine position of geometry
  • themes: default themes for axes, lines, grids, etc.

mpg |>
  ggplot(aes(y = hwy, x = displ, shape=manufacturer)) +
  geom_point(color = "royalblue")

mpg |>
  ggplot(aes(y=hwy, x=displ)) +
  geom_point() + 
  geom_smooth()

mpg |>
  ggplot(aes(y=hwy, x=displ, color=drv)) + 
  geom_point() + 
  geom_smooth()

mpg |>
  ggplot(aes(y=hwy, x=displ)) + 
  geom_point(aes(color=drv)) + 
  geom_smooth()

mpg |>
  ggplot(aes(x = class, fill = class)) +
  geom_bar()

ICMA 10/14/2025

ggplot(mpg, aes(x=displ, y=hwy)) +
  geom_point(aes(color = class)) + 
  geom_smooth(se=FALSE) +
  labs(
    x = "Engine displacement (L)",
    y = "Highway fuel economy (mpg)",
    color = "Car type",
    title="Fuel efficiency generally decreases with engine size",
    subtitle="Two seaters (sports cars) are an exception because of their light weight",
    caption="Data from fueleconomy.gov"
  )

label_info <- mpg |>
  group_by(drv) |>
  arrange(desc(displ)) |>
  slice_head(n=1) |>
  mutate(
    drive_type = case_when(
      drv == "f" ~ "front=wheel drive",
      drv == "r" ~ "rear-wheel drive",
      drv == "4" ~ "4-wheel drive"
    )
  ) |>
  select(displ, hwy, drv, drive_type)

ggplot(mpg, aes(x=displ, y=hwy, color = drv)) +
  geom_point(alpha = 0.3) +
  geom_smooth(se = FALSE) +
  geom_text(
    data = label_info,
    aes(x = displ, y = hwy, label = drive_type),
    fontface = "bold", size = 5,
    hjust = "right", vjust="bottom"
  ) +
  theme(legend.position = "none")

ggplot(mpg, aes(x=displ, y=hwy, color)) +
  geom_point() +
  annotate(
    geom = "label", x = 3.5, y = 38,
    label = str_wrap("LArger engine sizes tend to have lower fuel economy", width = 30),
    hjust = "left", color = "red"
  ) + 
  annotate(
    geom = "segment",
    x = 3, y = 35
  )

Error in `annotate()`:
! Problem while setting up geom.
ℹ Error occurred in the 3rd layer.
Caused by error in `compute_geom_1()`:
! `geom_segment()` requires the following missing aesthetics: xend or yend.
Run `]8;;x-r-run:rlang::last_trace()rlang::last_trace()]8;;` to see where the error occurred.

ggplot(mpg, aes(X=displ, y=hwy)) +
  geom_point(aes(color=drv)) +
  geom_smooth() +
  cor_cardinates(xlim = 4, ylim=5)

Error in cor_cardinates(xlim = 4, ylim = 5) : 
  could not find function "cor_cardinates"

ICMA 10/21/2025

my_values <- c(1,3,5,NA,8,10)
mean(my_values)

[1] NA

mean(my_values, na.rm=TRUE)

[1] 5.4

is.na(my_values)

[1] FALSE FALSE FALSE  TRUE FALSE FALSE

flights |> filter(is.na(dep_time))

flights |>
  group_by(year, month, day) |>
  summarize(
    proportion_delayed = mean(dep_delay <= 60, na.rm=TRUE),
    count_long_delay = sum(arr_delay <= 300, na.rm=TRUE),
    .groups = "drop"
  )

x <- c(-3:3, NA)
if_else(x > 0, "+ve", "-ve", "???")

[1] "-ve" "-ve" "-ve" "-ve" "+ve" "+ve" "+ve" "???"

case_when(
  x == 0 ~ "0",
  x < 0 ~ "-ve",
  x > 0 ~ "+ve",
  .default = "???"
)

[1] "-ve" "-ve" "-ve" "0"   "+ve" "+ve" "+ve" "???"

ICMA 10/21/2025

ICMA 10/30/2025

  ({{ x }} - min({{ x }}, na.rm = TRUE)) / (max({{ x }}, na.rm=TRUE) = min({{ x }}, na.rm=TRUE))

Error: object 'x' not found

flights |> summarize(
    across(c(dep_delay, arr_delay, air_time),
           list(
             median = \(x) median(x, na.rm=TRUE),
             mean = \(x) mean(x, na.rm=TRUE),
           )),
    n=n()
  )

Error in `summarize()`:
ℹ In argument: `across(...)`.
Caused by error in `list()`:
! argument 3 is empty
Run `]8;;x-r-run:rlang::last_trace()rlang::last_trace()]8;;` to see where the error occurred.

ICMA 11/04/2025

flights$carrier

flights[[10]]

flights[["carrier"]]

flights["carrier"]

animals <- c("giraffe", "zebra", "monkey")

for (name in animals) {
  print(name)
}

for (i in seq_along(animals)) {
  print(animals[[i]])
}

ICMA 11/11/2025